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272753253 | 2409.12399 | 2024-09-19 | I2I-Galip: Unsupervised Medical Image Translation Using Generative Adversarial CLIP | Unpaired image-to-image translation is a challenging task due to the absence of paired examples, which complicates learning the complex mappings between the distinct distributions of the source and target domains. One of the most commonly used approach for this task is CycleGAN which requires the training of a new pair... | [
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272753259 | 2409.12917 | 2024-09-19 | Training Language Models to Self-Correct via Reinforcement Learning | Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision. To address th... | [
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272753461 | 2409.12408 | 2024-09-19 | Mutual Information-based Representations Disentanglement for Unaligned Multimodal Language Sequences | The key challenge in unaligned multimodal language sequences lies in effectively integrating information from various modalities to obtain a refined multimodal joint representation. Recently, the disentangle and fuse methods have achieved the promising performance by explicitly learning modality-agnostic and modality-s... | [
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272753499 | 2409.12403 | 2024-09-19 | Preference Alignment Improves Language Model-Based TTS | Recent advancements in text-to-speech (TTS) have shown that language model (LM)-based systems offer competitive performance to their counterparts. Further optimization can be achieved through preference alignment algorithms, which adjust LMs to align with the preferences of reward models, enhancing the desirability of ... | [
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272753078 | 2409.12545 | 2024-09-19 | Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment | Knowledge distillation (KD) is an effective model compression method that can transfer the internal capabilities of large language models (LLMs) to smaller ones. However, the multi-modal probability distribution predicted by teacher LLMs causes difficulties for student models to learn. In this paper, we first demonstra... | [
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272832329 | 2409.15371 | 2024-09-19 | MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure | Low-Rank Adaptation (LoRA) is a widely adopted technique for parameter-efficient fine-tuning, but its slow convergence has spurred the development of numerous variants. Nevertheless, existing methods often fail to improve performance, memory footprint, and computational efficiency simultaneously. To address this challe... | [
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272753034 | 2409.12777 | 2024-09-19 | TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories | Dynamic Magnetic Resonance Imaging (MRI) is a crucial non-invasive method used to capture the movement of internal organs and tissues, making it a key tool for medical diagnosis. However, dynamic MRI faces a major challenge: long acquisition times needed to achieve high spatial and temporal resolution. This leads to hi... | [
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272753177 | 2409.12728 | 2024-09-19 | PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis | Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal... | [
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272753108 | 2409.12477 | 2024-09-19 | ViolinDiff: Enhancing Expressive Violin Synthesis with Pitch Bend Conditioning | Modeling the natural contour of fundamental frequency (F0) plays a critical role in music audio synthesis. However, transcribing and managing multiple F0 contours in polyphonic music is challenging, and explicit F0 contour modeling has not yet been explored for polyphonic instrumental synthesis. In this paper, we prese... | [
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272753217 | 2409.12677 | 2024-09-19 | (Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers | Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains, including machine learning models and human decision-makers in real-world applications. This involves calculating the disparities between probabilistic outcomes among social groups, such as acceptance rates ... | [
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272753106 | 2409.12602 | 2024-09-19 | Enhancing Agricultural Environment Perception via Active Vision and Zero-Shot Learning | Agriculture, fundamental for human sustenance, faces unprecedented challenges. The need for efficient, human-cooperative, and sustainable farming methods has never been greater. The core contributions of this work involve leveraging Active Vision (AV) techniques and Zero-Shot Learning (ZSL) to improve the robot's abili... | [
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272770194 | 2409.13094 | 2024-09-19 | DenoMamba: A fused state-space model for low-dose CT denoising | Low-dose computed tomography (LDCT) lower potential risks linked to radiation exposure while relying on advanced denoising algorithms to maintain diagnostic quality in reconstructed images. The reigning paradigm in LDCT denoising is based on neural network models that learn data-driven image priors to separate noise ev... | [
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272770729 | 2409.12995 | 2024-09-19 | Improving generalisability of 3D binding affinity models in low data regimes | Predicting protein-ligand binding affinity is an essential part of computer-aided drug design. However, generalisable and performant global binding affinity models remain elusive, particularly in low data regimes. Despite the evolution of model architectures, current benchmarks are not well-suited to probe the generali... | [
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272770461 | 2409.13025 | 2024-09-19 | Hardware-efficient quantum error correction via concatenated bosonic qubits | In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-... | [
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272753130 | 2409.12788 | 2024-09-19 | Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance | Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric. However, the value of optimal methods is not well understood yet, as the literature provides conflic... | [
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272770498 | 2409.13002 | 2024-09-19 | Across-Game Engagement Modelling via Few-Shot Learning | Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for instance, such AI models can supposedly learn to detect player actions across different games. Despite recent advancements in AI, domain gener... | [
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272753270 | 2409.12401 | 2024-09-19 | MambaRecon: MRI Reconstruction with Structured State Space Models | Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues, albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development of cutting-edge methods for the expedited reconstruction of MRI scans... | [
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272753494 | 2409.12415 | 2024-09-19 | Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues | We propose a multichannel-to-multichannel target sound extraction (M2M-TSE) framework for separating multichannel target signals from a multichannel mixture of sound sources. Target sound extraction (TSE) isolates a specific target signal using user-provided clues, typically focusing on single-channel extraction with c... | [
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272753278 | 2409.12397 | 2024-09-19 | Learning to Coordinate without Communication under Incomplete Information | Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. While communication helps, it is not always feasible. In this paper, we explore how effective coordination can be achieved without verbal communication,... | [
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272753284 | 2409.12468 | 2024-09-19 | Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation | Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it often struggles to cope with inconsistent and irrelevant information that can distract the LM from its tasks, especially when multiple evidenc... | [
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272753672 | 2409.12717 | 2024-09-19 | NDVQ: Robust Neural Audio Codec with Normal Distribution-Based Vector Quantization | Built upon vector quantization (VQ), discrete audio codec models have achieved great success in audio compression and auto-regressive audio generation. However, existing models face substantial challenges in perceptual quality and signal distortion, especially when operating in extremely low bandwidth, rooted in the se... | [
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272753362 | 2409.12629 | 2024-09-19 | Analysis of $\it{\Lambda}^\mathrm{0}_b \rightarrow pK^-\mu^+\mu^-$ decays | The differential branching fraction and angular coefficients of $\it{\Lambda}^\mathrm{0}_b \rightarrow pK^-\mu^+\mu^-$ decays are measured in bins of the dimuon mass squared and dihadron mass. The analysis is performed using a data set corresponding to 9$\text{fb}^{-1}$ of integrated luminosity collected with the $\mbo... | [
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272910908 | 2409.17172 | 2024-09-19 | What Would You Ask When You First Saw $a^2+b^2=c^2$? Evaluating LLM on Curiosity-Driven Questioning | Large language models (LLMs) can store a massive amount of knowledge, yet their potential to acquire new knowledge remains unknown. We propose a novel evaluation framework that evaluates this capability. This framework prompts LLMs to generate questions about a statement introducing scientific knowledge, simulating a c... | [
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272770660 | 2409.13004 | 2024-09-19 | Data Poisoning and Leakage Analysis in Federated Learning | Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating threats: {\em training data privacy intrusion} and {\em training data poisoning}. We first investigate training data privacy threat and pre... | [
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272753372 | 2409.12784 | 2024-09-19 | Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering | Despite the impressive success of text-to-image (TTI) generation models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by generation models fail to faithfully depict factual content. ... | [
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281113112 | 2409.12379 | 2024-09-19 | Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach | Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack surface, making 3D vision particularly vulnerable for safety-critical ... | [
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272753585 | 2409.12746 | 2024-09-19 | Bilingual Evaluation of Language Models on General Knowledge in University Entrance Exams with Minimal Contamination | In this article we present UNED-ACCESS 2024, a bilingual dataset that consists of 1003 multiple-choice questions of university entrance level exams in Spanish and English. Questions are originally formulated in Spanish and translated manually into English, and have not ever been publicly released. A selection of curren... | [
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272753624 | 2409.12809 | 2024-09-19 | Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration | Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human-AI collabo... | [
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272753416 | 2409.12884 | 2024-09-19 | Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage | Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise... | [
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272753517 | 2409.12524 | 2024-09-19 | Should RAG Chatbots Forget Unimportant Conversations? Exploring Importance and Forgetting with Psychological Insights | While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we propose LUFY, a simple yet effective method that focuses on emotionally arousing memories and retains... | [
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272753311 | 2409.12899 | 2024-09-19 | LI-GS: Gaussian Splatting with LiDAR Incorporated for Accurate Large-Scale Reconstruction | Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-G... | [
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272753260 | 2409.12954 | 2024-09-19 | GStex: Per-Primitive Texturing of 2D Gaussian Splatting for Decoupled Appearance and Geometry Modeling | Gaussian splatting has demonstrated excellent performance for view synthesis and scene reconstruction. The representation achieves photorealistic quality by optimizing the position, scale, color, and opacity of thousands to millions of 2D or 3D Gaussian primitives within a scene. However, since each Gaussian primitive ... | [
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272753026 | 2409.12470 | 2024-09-19 | HSIGene: A Foundation Model For Hyperspectral Image Generation | Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusio... | [
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272753601 | 2409.12512 | 2024-09-19 | Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models | Knowledge distillation (KD) is a technique that compresses large teacher models by training smaller student models to mimic them. The success of KD in auto-regressive language models mainly relies on Reverse KL for mode-seeking and student-generated output (SGO) to combat exposure bias. Our theoretical analyses and exp... | [
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272753405 | 2409.12887 | 2024-09-19 | Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning | Recently, using large language models (LLMs) for data augmentation has led to considerable improvements in unsupervised sentence embedding models. However, existing methods encounter two primary challenges: limited data diversity and high data noise. Current approaches often neglect fine-grained knowledge, such as enti... | [
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272753220 | 2409.12778 | 2024-09-19 | EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition | In this paper, we address the challenging problem of cross-modal (image-to-events) adaptation for event-based recognition without accessing any labeled source image data. This task is arduous due to the substantial modality gap between images and events. With only a pre-trained source model available, the key challenge... | [
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272770742 | 2409.13095 | 2024-09-19 | Examining Test-Time Adaptation for Personalized Child Speech Recognition | Automatic speech recognition (ASR) models often experience performance degradation due to data domain shifts introduced at test time, a challenge that is further amplified for child speakers. Test-time adaptation (TTA) methods have shown great potential in bridging this domain gap. However, the use of TTA to adapt ASR ... | [
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272753539 | 2409.12522 | 2024-09-19 | Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation | Deep learning based methods often suffer from performance degradation caused by domain shift. In recent years, many sophisticated network structures have been designed to tackle this problem. However, the advent of large model trained on massive data, with its exceptional segmentation capability, introduces a new persp... | [
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272987668 | 2409.18996 | 2024-09-19 | From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models | Cross-modal reasoning (CMR), the intricate process of synthesizing and drawing inferences across divergent sensory modalities, is increasingly recognized as a crucial capability in the progression toward more sophisticated and anthropomorphic artificial intelligence systems. Large Language Models (LLMs) represent a cla... | [
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272753681 | 2409.12507 | 2024-09-19 | Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks | Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information processing capability and event-driven nature make them ideally well-suited for neuromorphic datasets. However, current SNNs struggle to balance... | [
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272753483 | 2409.12428 | 2024-09-19 | Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift | Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of discriminatory behaviours exhibited by ML models. In this regard, fairness in machine learning has emerged as a priority research area. Cons... | [
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272753113 | 2409.12707 | 2024-09-19 | Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance | Fluidic injection provides a promising solution to improve the performance of overexpanded single expansion ramp nozzle (SERN) during vehicle acceleration. However, determining the injection parameters for the best overall performance under multiple nozzle operating conditions is still a challenge. The gradient-based o... | [
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272753700 | 2409.12705 | 2024-09-19 | Generation and Editing of Mandrill Faces: Application to Sex Editing and Assessment | Generative AI has seen major developments in recent years, enhancing the realism of synthetic images, also known as computer-generated images. In addition, generative AI has also made it possible to modify specific image characteristics through image editing. Previous work has developed methods based on generative adve... | [
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272753515 | 2409.12539 | 2024-09-19 | Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings | In radiation therapy (RT), the reliance on pre-treatment computed tomography (CT) images encounter challenges due to anatomical changes, necessitating adaptive planning. Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls short in tissue density accuracy. To address this, our innovative approach in... | [
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272753509 | 2409.12568 | 2024-09-19 | InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning | Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics. Despite the recognized importance, the Multimodal LLMs (MLLMs) field currently lacks a comprehensive open-source pre-training dat... | [
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272753221 | 2409.12953 | 2024-09-19 | JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images | Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perform well with only a shallow visual understanding by relying on background language biases. Thus, strong performance on these benchmarks does ... | [
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272753368 | 2409.12616 | 2024-09-19 | Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier Certificates | In modern robotics, addressing the lack of accurate state space information in real-world scenarios has led to a significant focus on utilizing visuomotor observation to provide safety assurances. Although supervised learning methods, such as imitation learning, have demonstrated potential in synthesizing control polic... | [
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272753741 | 2409.12880 | 2024-09-19 | Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models | E-commerce stores enable multilingual product discovery which require accurate product title translation. Multilingual large language models (LLMs) have shown promising capacity to perform machine translation tasks, and it can also enhance and translate product titles cross-lingually in one step. However, product title... | [
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272770654 | 2409.13096 | 2024-09-19 | Fast decision tree learning solves hard coding-theoretic problems | We connect the problem of properly PAC learning decision trees to the parameterized Nearest Codeword Problem ($k$-NCP). Despite significant effort by the respective communities, algorithmic progress on both problems has been stuck: the fastest known algorithm for the former runs in quasipolynomial time (Ehrenfeucht and... | [
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272753256 | 2409.12946 | 2024-09-19 | Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation | The robust self-training (RST) framework has emerged as a prominent approach for semi-supervised adversarial training. To explore the possibility of tackling more complicated tasks with even lower labeling budgets, unlike prior approaches that rely on robust pretrained models, we present SNORD - a simple yet effective ... | [
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272753319 | 2409.12493 | 2024-09-19 | ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring | We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for effi... | [
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272753576 | 2409.12405 | 2024-09-19 | On the Effectiveness of LLMs for Manual Test Verifications | Background: Manual testing is vital for detecting issues missed by automated tests, but specifying accurate verifications is challenging. Aims: This study aims to explore the use of Large Language Models (LLMs) to produce verifications for manual tests. Method: We conducted two independent and complementary exploratory... | [
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272753365 | 2409.12866 | 2024-09-19 | SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications | Large Language models have achieved impressive performance in automated software engineering. Extensive efforts have been made to evaluate the abilities of code LLMs in various aspects, with an increasing number of benchmarks and evaluation frameworks proposed. Apart from the most sought-after capability of code genera... | [
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272770433 | 2409.12993 | 2024-09-19 | CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair | Despite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper first presents an analysis of fine-tuned LLMs on Verilog coding, with synthetic data from prior methods. We identify two main issues: diffic... | [
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272753651 | 2409.12962 | 2024-09-19 | CLAIR-A: Leveraging Large Language Models to Judge Audio Captions | The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex task that requires considering diverse factors, among them, auditory scene understanding, sound-object inference, temporal coherence, and the ... | [
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272753455 | 2409.12558 | 2024-09-19 | RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues | In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG) - are often employed to enhance the quality of augmented generations in dialogues. These approaches often come with multi-tu... | [
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272770560 | 2409.13083 | 2024-09-19 | FedAT: Federated Adversarial Training for Distributed Insider Threat Detection | Insider threats usually occur from within the workplace, where the attacker is an entity closely associated with the organization. The sequence of actions the entities take on the resources to which they have access rights allows us to identify the insiders. Insider Threat Detection (ITD) using Machine Learning (ML)-ba... | [
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272753640 | 2409.12440 | 2024-09-19 | Incremental and Data-Efficient Concept Formation to Support Masked Word Prediction | This paper introduces Cobweb4L, a novel approach for efficient language model learning that supports masked word prediction. The approach builds on Cobweb, an incremental system that learns a hierarchy of probabilistic concepts. Each concept stores the frequencies of words that appear in instances tagged with that conc... | [
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272753702 | 2409.12854 | 2024-09-19 | Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging | Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhances patient outcomes but presents challenges in image quality and analysis scale. This paper introduces deep learning solutions for automate... | [
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272753190 | 2409.12648 | 2024-09-19 | Reconstruction of the Total Solar Irradiance during the last Millenium | Solar irradiance variations across various timescales, from minutes to centuries, represents a potential natural driver of past regional and global climate cold phases. To accurately assess the Sun's effect on climate, particularly during periods of exceptionally low solar activity known as grand minima, an accurate re... | [
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272753398 | 2409.12792 | 2024-09-19 | Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN | Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to... | [
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272753760 | 2409.12753 | 2024-09-19 | DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input | We propose DrivingForward, a feed-forward Gaussian Splatting model that reconstructs driving scenes from flexible surround-view input. Driving scene images from vehicle-mounted cameras are typically sparse, with limited overlap, and the movement of the vehicle further complicates the acquisition of camera extrinsics. T... | [
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272753487 | 2409.12797 | 2024-09-19 | Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing | Invariant causal prediction (ICP) is a popular technique for finding causal parents (direct causes) of a target via exploiting distribution shifts and invariance testing (Peters et al., 2016). However, since ICP needs to run an exponential number of tests and fails to identify parents when distribution shifts only affe... | [
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272753502 | 2409.12597 | 2024-09-19 | LARE: Latent Augmentation using Regional Embedding with Vision-Language Model | In recent years, considerable research has been conducted on vision-language models that handle both image and text data; these models are being applied to diverse downstream tasks, such as "image-related chat," "image recognition by instruction," and "answering visual questions." Vision-language models (VLMs), such as... | [
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273163238 | 2410.02808 | 2024-09-19 | KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation | AI-based vascular segmentation is becoming increasingly common in enhancing the screening and treatment of ophthalmic diseases. Deep learning structures based on U-Net have achieved relatively good performance in vascular segmentation. However, small blood vessels and capillaries tend to be lost during segmentation whe... | [
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272753410 | 2409.12538 | 2024-09-19 | PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation | Generating interdisciplinary research ideas requires diverse domain expertise, but access to timely feedback is often limited by the availability of experts. In this paper, we introduce PersonaFlow, a novel system designed to provide multiple perspectives by using LLMs to simulate domain-specific experts. Our user stud... | [
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272753558 | 2409.12432 | 2024-09-19 | Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms | Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these obstacles, Variational Quantum Algorithms (VQAs) are considered promising applications for present-day Noisy Intermediate-Scale Quantum (NISQ) systems. VQAs require multiple optimization iterations to converge ... | [
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272753628 | 2409.12769 | 2024-09-19 | The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning | Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the comm... | [
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272770618 | 2409.13045 | 2024-09-19 | TACE: Tumor-Aware Counterfactual Explanations | The application of deep learning in medical imaging has significantly advanced diagnostic capabilities, enhancing both accuracy and efficiency. Despite these benefits, the lack of transparency in these AI models, often termed "black boxes," raises concerns about their reliability in clinical settings. Explainable AI (X... | [
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272753287 | 2409.12514 | 2024-09-19 | TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation | Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA models face significant challenges: they are slow during inference and require extensive pre-training on large amounts of robotic data, maki... | [
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272753689 | 2409.12726 | 2024-09-19 | Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User Anomalies | Ensuring the security of cloud environments is imperative for sustaining organizational growth and operational efficiency. As the ubiquity of cloud services continues to rise, the inevitability of cyber threats underscores the importance of preemptive detection. This paper introduces a pioneering time-based embedding a... | [
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272753754 | 2409.12640 | 2024-09-19 | Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries | We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than... | [
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272753370 | 2409.12587 | 2024-09-19 | Test-Time Augmentation Meets Variational Bayes | Data augmentation is known to contribute significantly to the robustness of machine learning models. In most instances, data augmentation is utilized during the training phase. Test-Time Augmentation (TTA) is a technique that instead leverages these data augmentations during the testing phase to achieve robust predicti... | [
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